International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004
The pixels number is selected as the evaluation of the extraction
precision and the detailed steps are described as followings:
(1) Calculate semi-automatic extraction area M, pixels and
actual area M; pixels manually selected of the residential area
(2) Superpose M, and M» and measure the right extraction
pixels number, the error extraction pixels number and the miss
extraction pixels number, calculate correspondence percent.
Table 3 shows the precision results.
Table 3 Comparison of the semi-automatic extraction (Unit: Pixel)
Neural-Network Structure on a Multi-spectral Land-use/Land-
Cover Classification, Photogrammetry Engineering & Remote
sensing, 1997, 63(5):535-544
Image M M Right Error Missing Right Error Missing
No : 2 Arca Arca Arca Ratio% | Ratio% Ratio%
4a 22660 | 21246 20670 1990 576 91.2 8.8 2.7
4b 3221 13465 12825 396 640 97.0 3.0 4.8
4c 19235 | 19254 17944 1291 1310 93.2 6.8 6.8
4d 8535 7885 7722 813 163 90.5 9.5 2.1
4e 55183 | 39670 54171 1012 5499 98.2 1.8 9.2
As Table 3 shown, the right extraction ratios are all higher than
90%. The average right percent is over than 94.2% and this can
meet the requirements of the actual applications.
4. CONCLUSIONS AND DISCUSSIONS
The semi-automatic extraction results of residential areas front
different types of high resolution images show that the method
is simple and efficient and only takes very short time with the
94.2% correctness to the residential area extraction. And the
residential areas extracted by this method are available to
provide information for some application such as planning and
decision-making. To improve this method to high efficient, the
seed selecting should be done with the support of the neighbor
characters of the pixel distribution to obtain the better region
growing results by the computer.
ACKNOWLEDGEMENT
The author would like to thank Photogrammetry research group
to provide experiment images.
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